Development of a technique to identify advertisements in a video signal

Abstract

In recent years Content Based Information Retrieval (CBIR) has received a lot of research attention, starting with audio, followed by images and video. Video fingerprinting is a CBIR technique that creates a digital descriptor, also known as a fingerprint, for videos based on its content. These fingerprints are then saved to a database and used to detect unknown videos by comparing the unknown video's fingerprint to the fingerprints in the database to get a match. Many techniques have already been proposed with various levels of success, but most of the existing techniques focus mainly on robustness and neglect the speed of implementation. In this dissertation a novel video fingerprinting technique will be developed with the main focus on detecting advertisements in a television broadcast. Therefore the system must be able to process the incoming video stream in real-time and detect all the advertisements that are present. Even though the algorithm has to be fast, it still has to be robust enough to handle a moderate amount of distortions. These days video fingerprinting still holds many challenges as it involves characterizing videos, made up of sequences of images, effectively. This means the algorithm must somehow imitate the inherent ability of humans to recognize a video almost instantly. The technique uses the content of the video to derive a fingerprint, thus the features used by the fingerprinting algorithm should be robust to distortions that don't affect content according to humans.